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    A Bayesian spatio-temporal statistical analysis of Out-of-Hospital Cardiac Arrests

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    Draft_arresti_INLA_6.pdf
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    Description:
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    Type
    Article
    Authors
    Peluso, Stefano
    Mira, Antonietta
    Rue, Haavard cc
    Tierney, Nicholas John
    Benvenuti, Claudio
    Cianella, Roberto
    Caputo, Maria Luce
    Auricchio, Angelo
    KAUST Department
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-02-03
    Online Publication Date
    2020-02-03
    Print Publication Date
    2020-07
    Embargo End Date
    2021-01-20
    Permanent link to this record
    http://hdl.handle.net/10754/661091
    
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    Abstract
    We propose a Bayesian spatio-temporal statistical model for predicting Out-of-Hospital Cardiac Arrests (OHCA). Risk maps for Ticino, adjusted for demographic covariates, are built for explaining and forecasting the spatial distribution of OHCAs and their temporal dynamics. The occurrence intensity of the OHCA event in each area of interest, and the cardiac risk-based clustering of municipalities are efficiently estimated, through a statistical model that decomposes OHCA intensity into overall intensity, demographic fixed effects, spatially structured and unstructured random effects, time polynomial dependence and spatio-temporal random effect. In the studied geography, time evolution and dependence on demographic features are robust over different categories of OHCAs, but with variability in their spatial and spatio-temporal structure. Two main OHCA incidence-based clusters of municipalities are identified.
    Citation
    Peluso, S., Mira, A., Rue, H., Tierney, N. J., Benvenuti, C., Cianella, R., … Auricchio, A. (2020). A Bayesian spatiotemporal statistical analysis of out-of-hospital cardiac arrests. Biometrical Journal, 62(4), 1105–1119. doi:10.1002/bimj.201900166
    Sponsors
    Financial support from Fondazione Fratelli Agostino Enrico Rocca is acknowledged. Antonietta Mira was partially supported by SNF grant 105218 166504. The authors thank the Swiss Cardiology Foundation.
    Publisher
    Wiley
    Journal
    Biometrical Journal
    DOI
    10.1002/bimj.201900166
    ae974a485f413a2113503eed53cd6c53
    10.1002/bimj.201900166
    Scopus Count
    Collections
    Articles; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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